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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Published on: December 15, 2014

Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI).

Ioannis Delakis¹, Omer Hammad, Richard I Kitney

¹Department of Bioengineering, Imperial College, London, UK. i.delakis@imperial.ac.uk

Physics in Medicine and Biology

|August 1, 2007

Summary

This study introduces a novel wavelet-based de-noising algorithm for parallel magnetic resonance imaging (MRI). The method effectively reduces noise in MRI scans, preserving image details and spatial resolution, even with varying noise levels.

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Published on: December 9, 2010

Area of Science:

Medical Imaging
Signal Processing
Biomedical Engineering

Background:

Wavelet-based de-noising enhances magnetic resonance imaging (MRI) quality but assumes uniform noise.
Parallel MRI techniques introduce spatially varying noise, challenging traditional de-noising methods.
Existing methods often require coil sensitivity profiles or noise matrices, limiting clinical application.

Purpose of the Study:

To develop and evaluate a new automated wavelet-based de-noising algorithm for parallel MRI.
To address the challenge of spatially varying noise in parallel MRI acquisitions.
To improve image quality in clinical MRI settings without complex preprocessing.

Main Methods:

A novel algorithm was developed that extracts image edges and generates a spatially varying noise map from wavelet coefficients.
Noise map is refined by zeroing regions with detected edges and using directional analysis for low-contrast edges.
The algorithm was applied to phantom and brain images and compared against existing de-noising techniques.

Main Results:

The proposed algorithm demonstrated comparable performance to other techniques in high-noise central image areas.
Finer details and edges were effectively preserved in low-noise peripheral image areas.
The methodology proved fully automated, eliminating the need for sensitivity profiles or noise matrices.

Conclusions:

The new algorithm successfully de-noises parallel MRI images with spatially varying noise.
It maintains image quality and spatial resolution, outperforming traditional methods in preserving details.
Its automated nature and independence from coil parameters make it suitable for clinical MRI implementation.